Resource-Aware LLM Reasoning for Mobile Edge General Intelligence

Researchers propose a joint optimization framework for deploying large language model reasoning on resource-constrained edge devices, combining adaptive chain-of-thought prompting with distributed mixture-of-experts architecture. The framework dynamically balances reasoning quality and computational efficiency by treating reasoning depth as an optimizable network resource, achieving 90% accuracy and latency satisfaction with minimal inference overhead.

This research addresses a critical infrastructure challenge in AI deployment: running sophisticated reasoning models on edge devices with limited computational budgets. As LLMs increasingly power autonomous decision-making systems, the ability to execute complex reasoning at network edges—rather than relying on centralized cloud infrastructure—becomes strategically important for latency-sensitive applications and privacy-critical use cases. The proposed approach treats reasoning depth as a dynamic optimization variable alongside traditional networking concerns like transmission power and expert network activation, introducing a novel perspective on resource allocation.

The technical innovation stems from recognizing that not all tasks require maximum reasoning depth. By adaptively adjusting the complexity of chain-of-thought prompting based on task requirements and device capabilities, the system avoids wasteful computation while maintaining acceptable performance thresholds. The integration of mixture-of-experts architectures enables selective activation of model components, reducing memory footprint and power consumption on edge hardware.

For developers and infrastructure providers, this research validates that sophisticated AI reasoning isn't exclusively tied to data center deployment. Mobile and edge devices can execute meaningful reasoning tasks with proper architectural optimization. This opens pathways for on-device AI applications in autonomous vehicles, IoT systems, and mobile robotics where latency and privacy constraints make cloud-dependent solutions impractical. The experimental validation achieving 90% accuracy targets with sub-second additional latency demonstrates practical feasibility rather than theoretical potential.

• →Joint optimization of reasoning depth, expert activation, and transmission power enables efficient LLM reasoning on edge devices.
• →Adaptive chain-of-thought prompting allows dynamic adjustment of reasoning complexity based on task and device capabilities.
• →Distributed mixture-of-experts architecture reduces memory and power consumption compared to full model deployment.
• →Framework achieves 90% accuracy and latency satisfaction rates with less than one second additional inference time.
• →Research validates practical viability of deploying sophisticated AI reasoning in resource-constrained mobile edge environments.